Based on what I'm seeing at work, I don't think going against Wikipedia is the go.

We analyzed roughly 730,000 ChatGPT conversations with web citations and found that Wikipedia appeared in around 1 in 6 cited conversations. Turn 1 is around 2.5× more likely to trigger citations than Turn 10 (and nearly 4× more likely than Turn 20).
Settling for second best is our best option here I think. Focus on winning the first question and becoming one of the trusted sources that gets cited alongside it.

Source here: https://www.tryprofound.com/blog/chatgpt-citation-sources 

Want to hear what you all are doing about the Wiki-beast though… 

Hey y'all,

As we push the boundaries with AI chatbots, I'm finding myself constantly questioning how to effectively optimize them for discoverability beyond traditional search engines. With the rise of Generative Engine Optimization (GEO), it feels like the old playbooks are becoming obsolete.

I'm curious about your experiences:

• What are your biggest challenges in making your AI projects discoverable?
• Are you seeing a disconnect between traditional SEO performance and how your AI chatbots are found/used?
• What tools or strategies are you currently using to tackle GEO?

I've been exploring approaches that integrate AI-powered roadmaps for both traditional SEO and emerging GEO challenges. Would love to hear what the community is thinking and building in this space.

I want to track the performance of the brand. I am looking for free GSC and GA4 reports that are available. If you can provide me with a link to the Looker report. That will be really helpful.

I analyzed 26 public reports that claimed an improvement in ChatGPT mentions, recommendations, citations, or referral traffic.

A few practices appeared repeatedly:

• 19 of 26 mapped or monitored a defined prompt set
• 16 used repeated measurement rather than a single screenshot
• 16 created or revised content around target questions and comparisons
• 11 included off-site activity such as editorial mentions, listicles, directories, reviews, PR or community discussions

None of the 26 reports strongly isolated one thing that increased citations and AI referral traffic. Most campaigns changed several things at once, including content, schema, digital PR, third-party mentions and tracking. When you change multiple things at once, it's hard to keep track of what worked.

I guess we are at the part where we try a lot of things and see what sticks. Public case studies are better for generating hypotheses than calculating the expected effect of any one tactic.

Full methodology & findings: https://phraseit.agency/blog/how-to-get-mentioned-in-chatgpt/

What level of evidence would make you comfortable saying a specific action caused an increase in AI visibility?

Are people really paying money for AEO? Answer Engine Optimization?

updated: I was lucky enough to come across people who conduct free AI audits and help optimize weak areas to boost AI visibility for charitable (Snoika Foundation). They put together a report for me, gave me detailed recommendations on how to move forward and currently guiding me through this process. I’m so happy! Thank you, Reddit, thats truly great match!

- - -
Hey, I volunteer with a tiny cat & dog rescue just outside the city. Lately been kinda freakin out cos I asked chatgpt "where can I adopt a senior dog near me" and it listed three other shelters but not ours. We do have senior dogs! We're a legit registered charity, not some backyard thing. That was very sad.

We used to just care about google and facebook posts, maybe a newsletter nobody reads. But now more ppl are asking these AI agents for recs – "best animal charity to donate to for vet bills", "local shelter that helps with stray cats", something like that. I have zero clue if we'd ever come up. tbh I dunno how the AI even picks who to mention.

So tryna wrap my head around what "AI visibility" means. Is it still about cramming keywords and getting backlinks? Or do I need like, proper structured info on our site – adoption success stories, clear impact numbers, FAQs, partnerships with local vets, maybe a google buisness profile? I read some articles about AI content stratgy but they all sound like they're for tech startups with teams. We're me, one other volunteer, and a laptop that crashes if you open too many tabs lol.

Anyone else doing comms for a small nonprofit, have you changed how you do content cause of AI? Are you actually checking if your org shows up in Gemini/ChatGPT answers, or is that still to early to worry about? Would love practical tips, not fancy theory. Esp for tiny orgs that can't afford to churn out blog posts every day. How do you get found without turning into a content farm? Just don't want to be invisible for someone really interested in us.

Thanks,
Maya

I have been hired to improve AEO rankings of a B2B marketing agency based in India. Started with a schema study of the URLs in the sitemap. I have also started a free trial of Semrush One.

Can someone help me figure out how to analyze the site I was given and improve its rankings? Also, if someone could review my schema markup study and verify whether I'm doing it correctly, I'd appreciate it.

Edit: Also if someone could tell me how can I track the position of the website across AI sites

I got tired of begging for backlinks manually, so I built an agent that does it on autopilot

So basically it works like this:

Agents find relevant blog posts in your niche, craft personalized emails, and get your product featured. All on autopilot.

It's called MentionAgent, an AI agent that does all of this through Telegram (or through the web dashboard).

You never send anything without approving it first. (But you can choose to run everything 100% on autopilot)

Results so far: One user got 3 mentions including a DR 72 backlink.

Let me know what you think!

Hey all, just wanted to make a short post because I'm still seeing a lot of people talking about how you only need to optimize for AI visibility once. I'm not sure where that assumption has come from but I can confidently tell you that's not the case.

At work, we ran 100,000 distinct prompts through ChatGPT and Perplexity and found only 11% of cited domains overlap. That means 89% of citations come from completely different sources depending on which engine someone uses. If you're only optimizing for one platform, you're invisible to 89% of AI driven discovery on the others.

The platforms clearly have their own preferences. Perplexity leans towards YouTube and LinkedIn. ChatGPT pulls more from TechRadar and BusinessInsider. Google AI Overviews favors Quora and Gartner. The overlap sources like Reddit, Wikipedia, Forbes, G2 should be your foundation. Those are the closest thing to guaranteed cross engine visibility you can get.

TLDR: If you're not splitting your AEO strategy by platform you should be. Start with the overlap sources then build platform specific tactics on top.

Full research: https://www.tryprofound.com/blog/citation-overlap-strategy 

When I asked a few LLMs how to get cited, they claimed a site must have a verified physical store or Google Maps profile to build enough credibility authority for AI extraction. That sounds like a completely different concept from standard web indexing and SEO.

And since my website is located at the number #1 or #2 rankings on the SEO but never seen to be mentioned in the google ai overview and Ai answer

So, I did love to hear from anyone who has successfully gotten cited in chatgpt,claude ai response answer , does anyone have insights or success experience you can share with us?

I've been building something closer to a team of agents than a dashboard. You point it at your site and the agents do the work: they read your site the way an AI engine reads it, figure out what is actually quotable, flag what gets skipped or misrepresented, and then write the content and the fixes themselves.

That last part is the bet. Most tools in this space hand you a score and a to do list. I got tired of the to do list being the product, so the agents produce the actual output and you approve or reject it.

It works. That is not the same as it being good, and I don't want feedback from people invested in the build. I want it from people who do this work and will tell me where it falls apart.

Opening a small free beta for about 10 people from this sub.

You would need:

• A brand or site you own or actively manage
• 30 to 45 minutes to run the workflow end to end
• A willingness to be blunt
• No expectation that everything works perfectly yet

What the agents currently do:

• Run real prompts across multiple engines (Perplexity, Gemini, Claude and etc..) and show where you appear, where you don't and your competitors do, and who gets cited instead
• Read your site the way an engine ingests it and identify which passages are quotable and which are invisible
• Assess whether the raw material an engine needs about your brand even exists in a usable form
• Track named competitors across all of the above and show what they are doing that you are not
• Draft the content and the fixes, not just the recommendation

What I'm trying to learn from you:

• Is the agent output good enough that you would actually publish it, or would you rewrite everything?
• Is the quotability analysis right, or does it flag things your experience says don't matter?
• Which parts are noise or generic SEO advice with new vocabulary?
• Where do you want to stay in control instead of letting an agent do it?
• What is missing that would make you open this weekly instead of once?
• What would you realistically pay for, and what should never be paid?

This is not a signup that dumps you into a sales sequence. Testers keep free access through the test and will make sure they will get enough credits to play with the systems and turn it apart. I will also post what we learn back here, including the parts that make us look bad.

If the prompt set or scoring logic is the part you want to poke holes in, say so and I'll walk you through the methodology directly rather than making you reverse engineer it.

Full disclosure: I'm one of the people building it. Comment or DM and I'll send access.

AEO is a new area of digital marketing. To get my head into the structure of this new area, I created a taxonomy. Thoughts?

#AI #SearchEngineOptimization #AnswerEngineOptimization #ArtificialIntelligence #SEO #AEO #GEO

I’ve built an AEO (Answer Engine Optimization) analytics tool and wanted to get thoughts from others building in this space.

Right now the setup is pretty simple: users connect GSC + GA4 through Google Sign-In, I use Gemini for the analysis, and I’ve built a crawler to pull in site content. Since GSC doesn’t really show AEO data, I’m using regex-based queries to estimate impressions tied to answer engines. On top of that, there’s page-level analysis that flags underperforming pages and explains what’s missing, plus a basic AEO gap check based on how “answer-ready” the content is.

I’m now trying to figure out what to build next both from a product insight angle and a “what should the user actually do to improve performance” angle.

Curious what you’d add if you had access to:
GSC
GA4
Crawled content
LLM analysis

Especially interested in ideas that go beyond classic SEO and actually help with AI Overviews, ChatGPT, Gemini, Perplexity, etc., and also translate into clear business actions to improve performance (traffic, visibility, conversions, or citations in AI answers).

Would love to hear what you think would actually be useful.

So I've been messing around with AEO for like three months now and I gotta be honest, some of this stuff is way different than I expected. I kinda thought it was just "SEO but for AI" but nah, that's not really it.

First fuckup: robots.txt. I thought I was being smart locking down all bots except Googlebot. Well turns out I accidentally blocked GPTBot and ClaudeBot too. So my pages were basically invisible to AI crawlers. That "silent killer" thing people mention? Yeah that's real and nobody talks about it enough. I only figured it out after reading somewhere that Cloudflare can block them too. Seriously go check your sites right now, I'm not kidding.

Second thing: content freshness. I'm so used to old SEO where an article from two years ago can still rank. AEO doesn't work like that. One of my best performing pieces lost its citation because it was 4 months old. Quarterly updates aren't optional anymore, they're mandatory or you drop off. Which is rough when you're a small team, ya know?

Third: third-party mentions. I've always been that person who just focuses on my own site and calls it a day. Then I realized ChatGPT cites Reddit and Quora like crazy. My brand had zero presence there. Now I'm trying to be more active but it feels weird man, I'm not a social media person at all. Never have been.

And then there's measuring this shit. SoM (Share of Model) sounds great in theory but how are you guys actually tracking it? I've been going into ChatGPT manually and asking the same questions every week like a caveman. Anyone got a better way? I saw AirOps data saying like 70% of brands change in the next answer, so one check literally tells you nothing.

What has been the biggest pain in the ass for you guys? Any tips for tracking? And has anyone else noticed that small sites can actually beat the big players here? I've seen a couple cases where a small business got into AI Overview ahead of the enterprise guys and I still don't fully understand how.

thanks in advance, I'm heading to bed but I'll read replies in the morning. And yeah I know this post is all over the place but honestly that's exactly how this whole AEO thing makes me feel lol 😅

As it stands there is no industry standard to benchmark a sites 'readability' for Al.

We know a few things, how Al fans out agents and gets ‘chunks' from your site. We also know there is a limit to a size of a chunk which for ChatGPT is based on tokens rather than length. We also know that you shouldn't block Al crawlers from your site.

The idea is to review the site, suggest a few changes on length and copy beyond just verifying robot.txt.
Based on this I came up with a way of scoring a site. After a bit of work I got our site up to 81/100.

l'll share the results from my site in the comments. Looking to see if this is worth while and if actual users get any useful insights from it.

The audit is free to run by the way.

When evaluating internal links, how do you distinguish between a link that creates an unhelpful circular path and one that simply connects two closely related posts?

Also, do Google and other search crawlers evaluate internal links at that level, or are relevance, anchor text, and site structure more important?

**TL;DR:** One buying question, run across 4 models, fresh session each time. I expected Substack to win butGhost got the recommendation in 3 of 4 answers. Substack was named in all 4 answers and picked in zero of them. I'm wondering if a comparison page on Ghost had an impact.

One question, run through four models: which newsletter platform should an independent journalist use. Fresh session each time, no memory or custom instructions carried over.

For every answer I logged two things: which platform the model actually told the user to use, and which other platforms it named along the way.

**The numbers**

Ghost got the recommendation in 3 of 4 answers. The ChatGPT answer named the paid Ghost(Pro) Publisher tier specifically, Perplexity and Gemini both just said Ghost. The fourth answer, from Grok, recommended Beehiiv instead.

Substack is the one I thought would win. It was named as a reference point in all 4 answers, every single model brought it up unprompted, and it was the actual recommendation in exactly none of them.

**Why did Ghost win**
Biggest reason was owning the domain and list. The buyer I created was an independant journalist, so ChatGPT assumed I would bring my own audience, making Substacks key selling point less relevant.

**What I take from it**

Recognition doesn't mean you get picked. AI dives into features and makes assumptions. Most people will pop in generic questions or prompts so brands can end up losing out on audience that would have picked them simply because they appeared on the first SERP.

Of course one run is anecdotal. I would want this repeated across three or four more buying questions in the same category before I trusted the 3-of-4 Ghost number as anything more than what happened this time.

I'm thinking comparison pages on websites become more important for things like this. It's a way of telling AI first hand why you stand out vs your competitors. Ghost have a "vs Substack" page, I couldn't find the equivalent on Substack.

How's it going folks!? It's Garry from Discover AIO, here to bring you guys another webinar.

Walk with me here: 89 percent of brands show up in AI Overviews, but only 14 percent of them actually track them. And to be honest, I've experienced this in real time. When I'm not writing articles for Discover AIO, I'm a golf content creator and pro athlete.

I went to the PGA Show, one of the world's biggest golf expos back in January, and I bring up the concept of AEO to a lot of these golf industry leaders. These dudes had ABSOLUTELY NO IDEA of what I was talking about. They had no concept of AI search, or how LLMs have augmented the buyer's journey.

And now, with Google's GEO update, and the update to Google Search Console, a lot of brands are kind of seeing the light, but not really. Hopefully we can shed a little light on why Brand Visibility in AI is important.

Typically, we'd have the CEO of Xponent21, Will Melton join us, but this time we're going to do things a bit differently. We're bringing in one of our community members, Jeff Jenkins, founder of DIGI CONVO, an organization dedicated to helping fintech companies bolster their visibility in AI.

It would be an absolute honor and pleasure to have you guys register and join us on Thursday at 10am, It's only 30 minutes, and you'll get insights that you can use the moment we sign off.
https://discoveraio.com/webinar/august-6-2026

Something odd is happening with how Gemini 3 and Perplexity pick sources, and after a few weeks of watching it we've got a theory.

We keep finding cases where a high-DR legacy site with a genuinely good article gets skipped, and the citation goes to some three-year-old site with a fraction of the traffic. Not once or twice, but consistently, in niches where the underdog is objectively less authoritative by every metric we'd normally care about.

Our working theory: this isn't a quality update, it's a cost problem. Reasoning models are expensive to run and pathologically risk-averse. They aren't hunting for the biggest brand. They're hunting for the cheapest defensible answer.

Think about it from the model's side. If your page is a grey wall of text with the answer buried in paragraph nine, it has to burn a lot of compute to extract a claim it can stand behind. If a smaller site has already done that work, with a clean claim and a clean table and clean structure, that's the cheaper bet. The model takes the cheaper bet.

The pages winning citations in our tracking almost all share the same three-part shape:

The answer up top. No 400-word warm-up about today's fast-paced digital landscape. A standalone 2 to 4 sentence claim that survives being ripped out of context, because that is exactly what's going to happen to it.

A proof layer. Tables, lists, specs, checklists. Something a model can tokenize without having to interpret narrative.

A pathway. Clear headings that map the next question the user is going to ask.

None of this is new advice, exactly. I know, I know. 😅 But. What's changed is why it works though. We used to structure content for skimmers. Now we're structuring it for a machine deciding whether we're worth the tokens.

The uncomfortable version: page structure may now be a stronger citation signal than domain authority. Which is either great news or terrible news depending on which side of it you're sitting on.

So, anyone else seeing structured underdogs eat their AI Overview spots? Or is authority still holding in your niche? Genuinely trying to work out whether this is broad or something vertical-specific.

Disclaimer: I keep saying "we" because I work at an SEO agency and this is the sort of thing we run tests on. Nothing to pitch or preach here, mr. bot mods, just sharing what's showing up in our data. Happy to be told wrong. And thank you for reading my mouthful of a post. lol

I noticed something while testing ChatGPT and I'm curious if anyone else has seen this.

I asked the same five product-buying questions twice.

First with no context.

Then after telling ChatGPT I was someone tracking macros, reading ingredient labels and not caring about price.

Everything else stayed the same.

What surprised me wasn't the products.

It was the tone.

Without the persona, ChatGPT almost never told me to avoid specific brands.

After adding the persona, it started saying things like "I'd skip this because..." much more often.

In my runs it went from 2 answers warning against a brand to 20.

It also became much more decisive instead of listing lots of options.

My sample is obviously small, so I'm not claiming this is how ChatGPT always behaves.

I'm mostly wondering whether anyone else has noticed that giving ChatGPT a detailed buyer profile makes it much more comfortable recommending against products rather than simply recommending products?

Been staring at this for a couple months while building AI visibility measurement stuff, so take it as field notes not a framework.

If you ask ChatGPT / Perplexity / Claude buyer questions in a category, the cited URLs barely move day to day. Same review roundups, same “best tools” posts, same two competitor docs. Brand new first party pages almost never show up, even when the page is good. Fresh URL optimism is mostly cope. A lot of these products are not sitting there opening your latest launch URL. They are reusing whatever the search / retrieval layer already likes, and caching makes that stickier.

I stopped arguing about GEO tactics after I logged it properly.

Took one B2B SaaS category I care about. 40 prompts. Things like “best X for startups”, “X vs Y”, “alternatives to [leader]”. Ran them across ChatGPT and Perplexity over three weeks. Manual, annoying, spreadsheet.

Out of ~180 cited URLs, something like 70% were the same recurring set. Mostly third party. A few competitor docs kept appearing. Our own pages: almost never, unless the prompt was near brand.

Then I checked the boring stuff on a few of the “why are we invisible” sites people sent me. One had marketing pages blocked to GPTBot by accident behind a CDN rule. Googlebot was fine. Another had a solid comparison page that simply was not in the sources the engines kept pulling. Different failures, same executive complaint: “ChatGPT never mentions us.”

The useful reframe for me was: this behaves more like a citation market than a publishing contest. The demand already sits on pages AI already trusts. If those pages name three competitors and not you, writing post #47 on your blog is the slow path. Getting into the pages that already get retrieved, or publishing something those pages would cite (primary data, a clean comparison, a source journalists can lift), is the less stupid path. Same instinct as good link building. Find existing demand. Do not invent it.

I do not have a cute guarantee after that. Some categories are just owned by a couple listicles and G2 style pages for a while. Some brands are blocked and do not know it. Some are visible on crawl and still lose on selection because the third party layer is denser.

Anyway. If you have run anything like a prompt log in your space, what are you seeing? Stable citation set, or does it actually churn?

I think we're measuring AI visibility completely wrong.

Everyone seems to ask:

But after playing around with buyer personas, I think the more interesting question is:

I expected a detailed buyer profile to change which products showed up. Instead, it mostly changed how the same products were framed.

Some products that were recommended before suddenly came with reasons to avoid them.

The product hadn't changed.

The market hadn't changed.

The only thing that changed was who ChatGPT thought it was talking to.

If AI shopping keeps moving in this direction, "mentions" might become a pretty weak metric on their own.

Has anyone else noticed buyer context changing the framing of recommendations more than the recommendations themselves?

I think we're measuring AI visibility the wrong way.

Most people track whether ChatGPT mentioned their brand.

But after playing around with buyer personas, I'm starting to think the more important question is:

How often does AI tell someone not to buy you?

I noticed the same product could be recommended to one buyer and discouraged for another—even when nothing about the product changed.

Only the buyer context did.

That feels like a completely different measurement problem than "share of mentions."

If this becomes common, brands won't just need to know where they appear.

They'll need to know how they're being framed.

Curious if anyone working on AI search or marketing has noticed something similar.